Abstract
Masi entropy is a popular criterion employed for identifying appropriate threshold values in image thresholding. However, with an increasing number of thresholds, the efficiency of Masi entropy-based multi-level thresholding algorithms becomes problematic. To overcome this, we propose a novel differential evolution (DE) algorithm as an effective population-based metaheuristic for Masi entropy-based multi-level image thresholding. Our ME-GDEAR algorithm benefits from a grouping strategy to enhance the efficacy of the algorithm for which a clustering algorithm is used to partition the current population. Then, an updating strategy is introduced to include the obtained clusters in the current population. We further improve the algorithm using attraction (towards the best individual) and repulsion (from random individuals) strategies. Extensive experiments on a set of benchmark images convincingly show ME-GDEAR to give excellent image thresholding performance, outperforming other metaheuristics in 37 out of 48 cases based on cost function evaluation, 26 of 48 cases based on feature similarity index, and 20 of 32 cases based on Dice similarity. The obtained results demonstrate that population-based metaheuristics can be successfully applied to entropy-based image thresholding and that strengthening both exploitation and exploration strategies, as performed in ME-GDEAR, is crucial for designing such an algorithm.
Highlights
Image segmentation is a challenging task in machine vision
While bi-level image thresholding (BLIT) methods try to find a single threshold to discriminate between the background and foreground, multi-level image thresholding (MLIT) approaches have determined multiple threshold values to partition an image into several regions
We propose Masi entropy-based grouping differential evolution boosted by attraction and repulsion strategies (ME-GDEAR), as an improved DE algorithm for multi-level image thresholding
Summary
Image segmentation is a challenging task in machine vision. It is the process of dividing an image into several non-overlapping areas based on features such as colour or texture. Entropy-based MLIT algorithms have been extensively employed for image segmentation [13,14,15]. Masi entropy has shown remarkable performance for BLIT, but its efficiency drastically decreases when increasing the number of thresholds due to the resulting time complexity. To address this issue, population-based metaheuristic algorithms (PBMHs) such as differential evolution (DE) and particle swarm optimisation (PSO), where a population of candidate solutions is iteratively and co-operatively improved, offer a powerful alternative. Khairuzzaman et al [24] employ PSO with Masi entropy for image segmentation and shows that PSO can outperform the dragonfly algorithm (DA) on six benchmark images.
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